基于互信息最大化和混合模型的深度图聚类
Deep Graph Clustering via Mutual Information Maximization and Mixture Model
AI总结：
本文提出了一种对比学习框架，通过最大化互信息和混合高斯分布来学习适合聚类的节点嵌入，有效提升了图聚类性能。
摘要：
属性图聚类或社区检测在图分析中是一个具有挑战性的任务。本文介绍了一种对比学习框架，用于学习适合聚类的节点嵌入。尽管图对比学习在自监督图学习中表现出色，但将其应用于图聚类尚未被充分探索。我们提出了高斯混合信息最大化（GMIM），它利用互信息最大化方法进行节点嵌入，并假设表示空间遵循混合高斯（MoG）分布。我们的目标聚类部分尝试为每个社区拟合高斯分布。节点嵌入与MoG参数在统一框架中联合优化。在真实世界数据集上的实验验证了我们方法在社区检测中的有效性。
英文摘要：
Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. In this paper, we introduce a contrastive learning framework for learning clustering-friendly node embedding. Although graph contrastive learning has shown outstanding performance in self-supervised graph learning, using it for graph clustering is not well explored. We propose Gaussian mixture information maximization (GMIM) which utilizes a mutual information maximization approach for node embedding. Meanwhile, it assumes that the representation space follows a Mixture of Gaussians (MoG) distribution. The clustering part of our objective tries to fit a Gaussian distribution to each community. The node embedding is jointly optimized with the parameters of MoG in a unified framework. Experiments on real-world datasets demonstrate the effectiveness of our method in community detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
